GEA Analysis

1. LFMM

1.1 Individual sampling

1.1.1 Summary plots

K

walk(K_plots, grid.arrange)

TPRCOMBO

walk(TPR_plots, grid.arrange)

FDRCOMBO

walk(FDR_plots, grid.arrange)

### TOTALN

walk(TOTALN_plots, grid.arrange)

1.1.2 Model summaries

lfmm_ind <- 
  lfmm_ind %>% 
  filter(padj == "fdr") %>%
  filter(sig == 0.05) %>%
  filter(method == "ridge") %>%
  filter(K_selection == "tracy.widom")

run_lmer(lfmm_ind, "TPRCOMBO", filepath = here(p4path, "LFMM_individual_TPR.csv"))

Contrast	Estimate	SE	Z ratio	p
envgeo - grid	−0.0014	0.0023	−0.5884	0.9356357
envgeo - rand	0.0030	0.0023	1.2888	0.5700741
envgeo - trans***	0.0176	0.0023	7.5786	2.3 × 10−13***
grid - rand	0.0044	0.0023	1.8772	0.2378691
grid - trans***	0.0190	0.0023	8.1670	4.9 × 10−14***
rand - trans***	0.0146	0.0023	6.2899	1.9 × 10−9***
*** p < 0.001

run_lmer(lfmm_ind, "FDRCOMBO", filepath = here(p4path, "LFMM_individual_FDR.csv"))

Contrast	Estimate	SE	Z ratio	p
envgeo - grid	0.0074	0.0095	0.7829	0.86225851
envgeo - rand***	−0.0246	0.0095	−2.5990	0.04614817***
envgeo - trans**	−0.0760	0.0095	−8.0269	5.9 × 10−14**
grid - rand*	−0.0320	0.0095	−3.3819	4.0 × 10−3*
grid - trans**	−0.0834	0.0095	−8.8098	3.0 × 10−14**
rand - trans**	−0.0514	0.0095	−5.4278	3.4 × 10−7**
*** p < 0.05
** p < 0.001
* p < 0.01

run_lmer(lfmm_ind, "TOTALN", filepath = here(p4path, "LFMM_individual_TOTALN.csv"))

Contrast	Estimate	SE	Z ratio	p
envgeo - grid***	0.2065	0.0707	2.9201	0.01837934***
envgeo - rand	0.0656	0.0707	0.9279	0.78986890
envgeo - trans**	−0.7362	0.0707	−10.4098	4.3 × 10−14**
grid - rand	−0.1409	0.0707	−1.9921	0.19087871
grid - trans**	−0.9427	0.0707	−13.3299	0.0**
rand - trans**	−0.8018	0.0707	−11.3378	0.0**
*** p < 0.05
** p < 0.001

1.1.3 Megaplots

MEGAPLOT(lfmm_ind, "K.1", colpal = "turbo")

MEGAPLOT(lfmm_ind, "TPRCOMBO", colpal = "plasma")

MEGAPLOT(lfmm_ind, "FDRCOMBO", colpal = "viridis", direction = -1)

MEGAPLOT(lfmm_ind, "TOTALN", colpal = "viridis")

### 1.1.4 Latent factor versus sample number test

walk(K_plots, grid.arrange)

walk(TPR_plots, grid.arrange)

walk(FDR_plots, grid.arrange)

walk(TOTALN_plots, grid.arrange)

1.2 Site sampling

1.2.1 Summary plots

K

walk(K_plots, grid.arrange)

TPRCOMBO

walk(TPR_plots, grid.arrange)

FDRCOMBO

walk(FDR_plots, grid.arrange)

TOTALN

walk(TOTALN_plots, grid.arrange)

1.1.2 Model summaries

lfmm_site <- 
  lfmm_site %>% 
  filter(padj == "fdr") %>%
  filter(sig == 0.05) %>%
  filter(method == "ridge") %>%
  filter(K_selection == "tracy.widom")

run_lmer(lfmm_site, "TPRCOMBO", filepath = here(p4path, "LFMM_site_TPR.csv"))

Contrast	Estimate	SE	Z ratio	p
envgeo - equi	0.0042	0.0023	1.8455	0.15493454
envgeo - rand***	0.0091	0.0023	4.0177	1.7 × 10−4***
equi - rand	0.0049	0.0023	2.1723	0.07598666
*** p < 0.001

run_lmer(lfmm_site, "FDRCOMBO", filepath = here(p4path, "LFMM_site_FDR.csv"))

Contrast	Estimate	SE	Z ratio	p
envgeo - equi	−0.0057	0.0046	−1.2394	0.4298770
envgeo - rand	−0.0020	0.0046	−0.4349	0.9010300
equi - rand	0.0037	0.0046	0.8046	0.7001826

run_lmer(lfmm_site, "TOTALN", filepath = here(p4path, "LFMM_site_TOTALN.csv"))

Contrast	Estimate	SE	Z ratio	p
envgeo - equi***	6.7684	2.1833	3.1001	5.5 × 10−3***
envgeo - rand	4.3663	2.1833	1.9999	0.1122041
equi - rand	−2.4021	2.1833	−1.1002	0.5139103
*** p < 0.01

1.2.3 Megaplots

MEGAPLOT(lfmm_site, "K.1", colpal = "turbo")

MEGAPLOT(lfmm_site, "TPRCOMBO", colpal = "plasma")

MEGAPLOT(lfmm_site, "FDRCOMBO", colpal = "viridis", direction = -1)

MEGAPLOT(lfmm_site, "TOTALN", colpal = "viridis")

### 1.2.4 Latent factor versus sample number test

walk(K_plots, grid.arrange)

walk(TPR_plots, grid.arrange)

walk(FDR_plots, grid.arrange)

walk(TOTALN_plots, grid.arrange) 

2. RDA

2.1 Individual sampling

2.1.1 Summary plots

rda_ind <- format_rda(here(p3path, "rda_indsampling_results.csv"))
rda_ind_tidy <- 
  rda_ind %>%
  filter(padj == "fdr") %>%
  filter(sig == 0.05) %>%
  group_by(correctPC) %>%
  group_split()

walk(TPR_plots, grid.arrange)

walk(FDR_plots, grid.arrange)

2.1.2 Model summaries

rda_ind <- 
  rda_ind %>% 
  filter(padj == "fdr") %>%
  filter(sig == 0.05) %>%
  filter(correctPC == "FALSE") 

run_lmer(rda_ind, "TPRCOMBO", filepath = here(p4path, "RDA_individual_TPR.csv"))

Contrast	Estimate	SE	Z ratio	p
envgeo - grid	0.0004	0.0017	0.2367	0.9953359
envgeo - rand	0.0029	0.0017	1.7358	0.3050510
envgeo - trans***	0.0110	0.0017	6.6472	1.8 × 10−10***
grid - rand	0.0025	0.0017	1.4991	0.4380159
grid - trans***	0.0106	0.0017	6.4105	8.7 × 10−10***
rand - trans***	0.0081	0.0017	4.9114	5.4 × 10−6***
*** p < 0.001

run_lmer(rda_ind, "FDRCOMBO", filepath = here(p4path, "RDA_individual_FDR.csv"))

Contrast	Estimate	SE	Z ratio	p
envgeo - grid	−0.0010	0.0029	−0.3565	0.98447702
envgeo - rand	0.0017	0.0029	0.6110	0.92862133
envgeo - trans***	0.0085	0.0029	2.9768	0.01543605***
grid - rand	0.0028	0.0029	0.9675	0.76794265
grid - trans**	0.0095	0.0029	3.3333	4.8 × 10−3**
rand - trans	0.0067	0.0029	2.3658	0.08376928
*** p < 0.05
** p < 0.01

run_lmer(rda_ind, "TOTALN", filepath = here(p4path, "RDA_individual_TOTALN.csv"))

Contrast	Estimate	SE	Z ratio	p
envgeo - grid	0.0018	0.0247	0.0739	0.9998551
envgeo - rand	0.0406	0.0247	1.6478	0.3517771
envgeo - trans***	0.1505	0.0247	6.1052	6.2 × 10−9***
grid - rand	0.0388	0.0247	1.5738	0.3936324
grid - trans***	0.1487	0.0247	6.0313	9.8 × 10−9***
rand - trans***	0.1099	0.0247	4.4574	4.9 × 10−5***
*** p < 0.001

1.1.3 Megaplots

MEGAPLOT(rda_ind, "TPRCOMBO", colpal = "plasma")

MEGAPLOT(rda_ind, "FDRCOMBO", colpal = "viridis", direction = -1)

MEGAPLOT(rda_ind, "TOTALN", colpal = "viridis")

2.2 Site sampling

2.2.1 Summary plots

rda_site <- format_rda(here(p3path, "rda_sitesampling_results.csv")) 
rda_site_tidy <- 
  rda_site %>%
  filter(padj == "fdr") %>%
  filter(sig == 0.05) %>%
  group_by(correctPC) %>%
  group_split()

walk(TPR_plots, grid.arrange)

walk(FDR_plots, grid.arrange)

walk(TOTALN_plots, grid.arrange)

2.2.2 Model summaries

rda_site <- 
  rda_site %>% 
  filter(padj == "fdr") %>%
  filter(sig == 0.05) %>%
  filter(correctPC == "FALSE") 

run_lmer(rda_site, "TPRCOMBO", filepath = here(p4path, "RDA_site_TPR.csv"))

Contrast	Estimate	SE	Z ratio	p
envgeo - equi	−0.0005	0.0016	−0.2956	0.9529805
envgeo - rand***	0.0076	0.0016	4.6756	8.7 × 10−6***
equi - rand***	0.0080	0.0016	4.9712	2.0 × 10−6***
*** p < 0.001

run_lmer(rda_site, "FDRCOMBO", filepath = here(p4path, "RDA_site_FDR.csv"))

Contrast	Estimate	SE	Z ratio	p
envgeo - equi***	0.0085	0.0025	3.3981	2.0 × 10−3***
envgeo - rand**	0.0123	0.0025	4.9457	2.3 × 10−6**
equi - rand	0.0039	0.0025	1.5476	0.2687622
*** p < 0.01
** p < 0.001

run_lmer(rda_site, "TOTALN", filepath = here(p4path, "RDA_site_TOTALN.csv"))

Contrast	Estimate	SE	Z ratio	p
envgeo - equi	0.0288	0.0207	1.3945	0.3437613
envgeo - rand***	0.1160	0.0207	5.6115	6.0 × 10−8***
equi - rand***	0.0872	0.0207	4.2171	7.3 × 10−5***
*** p < 0.001

2.2.3 Megaplots

MEGAPLOT(rda_site, "TPRCOMBO", colpal = "plasma")

MEGAPLOT(rda_site, "FDRCOMBO", colpal = "viridis", direction = -1)

MEGAPLOT(rda_site, "TOTALN", colpal = "viridis")